SKATE: A Natural Language Interface for Encoding Structured Knowledge

TL;DR

SKATE is a natural language interface that refines user input into structured knowledge representations using neural parsing and semi-structured templates, improving interpretability and knowledge acquisition.

Contribution

The paper introduces SKATE, a novel NL interface combining neural semantic parsing with semi-structured templates for better knowledge encoding and interaction.

Findings

Preliminary coverage analysis for story understanding.

Successful integration with rule-generation for knowledge acquisition.

Application in COVID-19 policy design.

Abstract

In Natural Language (NL) applications, there is often a mismatch between what the NL interface is capable of interpreting and what a lay user knows how to express. This work describes a novel natural language interface that reduces this mismatch by refining natural language input through successive, automatically generated semi-structured templates. In this paper we describe how our approach, called SKATE, uses a neural semantic parser to parse NL input and suggest semi-structured templates, which are recursively filled to produce fully structured interpretations. We also show how SKATE integrates with a neural rule-generation model to interactively suggest and acquire commonsense knowledge. We provide a preliminary coverage analysis of SKATE for the task of story understanding, and then describe a current business use-case of the tool in a specific domain: COVID-19 policy design.